Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them
Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical informa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2022.913093/full |
| _version_ | 1818015030893871104 |
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| author | Ulrich von Ulmenstein Max Tretter David B. Ehrlich Christina Lauppert von Peharnik |
| author_facet | Ulrich von Ulmenstein Max Tretter David B. Ehrlich Christina Lauppert von Peharnik |
| author_sort | Ulrich von Ulmenstein |
| collection | DOAJ |
| description | Current technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic. |
| first_indexed | 2024-04-14T06:51:41Z |
| format | Article |
| id | doaj.art-b2b04aba16304512b2a1b27307e283d9 |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-04-14T06:51:41Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-b2b04aba16304512b2a1b27307e283d92022-12-22T02:07:00ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122022-08-01510.3389/frai.2022.913093913093Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address themUlrich von Ulmenstein0Max Tretter1David B. Ehrlich2Christina Lauppert von Peharnik3Chair of Public Law, Justus Liebig University of Giessen, Giessen, GermanyDepartment of Systematic Theology, Friedrich Alexander University of Erlangen Nuremberg, Erlangen, Bavaria, GermanyDepartment of Economics and Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, GermanyChair of Public Law, Justus Liebig University of Giessen, Giessen, GermanyCurrent technological and medical advances lend substantial momentum to efforts to attain new medical certainties. Artificial Intelligence can enable unprecedented precision and capabilities in forecasting the health conditions of individuals. But, as we lay out, this novel access to medical information threatens to exacerbate adverse selection in the health insurance market. We conduct an interdisciplinary conceptual analysis to study how this risk might be averted, considering legal, ethical, and economic angles. We ask whether it is viable and effective to ban or limit AI and its medical use as well as to limit medical certainties and find that neither of these limitation-based approaches provides an entirely sufficient resolution. Hence, we argue that this challenge must not be neglected in future discussions regarding medical applications of AI forecasting, that it should be addressed on a structural level and we encourage further research on the topic.https://www.frontiersin.org/articles/10.3389/frai.2022.913093/fullartificial intelligencehealthcare system (HCS)health insuranceadverse selectionmedical certainties |
| spellingShingle | Ulrich von Ulmenstein Max Tretter David B. Ehrlich Christina Lauppert von Peharnik Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them Frontiers in Artificial Intelligence artificial intelligence healthcare system (HCS) health insurance adverse selection medical certainties |
| title | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
| title_full | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
| title_fullStr | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
| title_full_unstemmed | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
| title_short | Limiting medical certainties? Funding challenges for German and comparable public healthcare systems due to AI prediction and how to address them |
| title_sort | limiting medical certainties funding challenges for german and comparable public healthcare systems due to ai prediction and how to address them |
| topic | artificial intelligence healthcare system (HCS) health insurance adverse selection medical certainties |
| url | https://www.frontiersin.org/articles/10.3389/frai.2022.913093/full |
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